{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d4e901e",
   "metadata": {},
   "source": [
    "# Markdown\n",
    "## Text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4889b06c",
   "metadata": {},
   "source": [
    "- fix point interation\n",
    "- bisection\n",
    "- Newton iteration\n",
    "- secant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a582f26",
   "metadata": {},
   "source": [
    "## Math equations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae0831b",
   "metadata": {},
   "source": [
    "### simple math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f35eecc",
   "metadata": {},
   "source": [
    "$1+1=2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55082ce",
   "metadata": {},
   "source": [
    "### greek letter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2268034",
   "metadata": {},
   "source": [
    "$\\alpha+\\beta=1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6edaaf3",
   "metadata": {},
   "source": [
    "### fractional\n",
    "$\\frac {1} {3}+\\frac {2} {3}=1$\n",
    "\n",
    "$\\frac {\\sigma} {3}+\\frac {\\gamma} {3}=1$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca99bc9",
   "metadata": {},
   "source": [
    "### root\n",
    "$\\sqrt -2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5907916b",
   "metadata": {},
   "source": [
    "### exponential\n",
    "$\\alpha ^ abcdefghijklmn$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77a68c0",
   "metadata": {},
   "source": [
    "### vector\n",
    "$\\vec{a}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19781874",
   "metadata": {},
   "source": [
    "## Image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "967b7596",
   "metadata": {},
   "source": [
    "[![](yuzi.jfif \"11\")](https://mmbiz.qpic.cn/mmbiz_jpg/DIs3G03FOLHmkjZJCsMWJ7ibIeJsd3fAKnpddhkviaodkh8TwJRuzOXwCSgNliafGMSEKt0iar5yHXRUAicemwibo6Pw/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5dd639ec",
   "metadata": {},
   "source": [
    " <img src=\"yuzi.jfif\" width = \"300\" height = \"200\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8cbd52",
   "metadata": {},
   "source": [
    " <img src=\"6cc52b1e368d614d.jpg\" width = \"300\" height = \"200\" alt=\"图片名称\" align=center />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b67918c",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b4f9e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist1=[1,2,3,4]\n",
    "mylist2=[2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29db8df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist1+mylist2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e2031b0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"int\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24416/3442381011.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmylist1\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
     ]
    }
   ],
   "source": [
    "mylist1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c84ed84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist1*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7abda13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "118a3968",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylista=np.array(mylist1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e78aa783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9bf2200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4535dc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aed4be3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista-mylista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e07d394a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista*mylista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52a2c686",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylistb=np.array([1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0cf5a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylistb+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71c2b3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   8,  27,  64, 125, 216], dtype=int32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylistb**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4b343e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "mylistb=np.array([2,2,2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "82c3df9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  0, -1, -2])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylistb-mylista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e23039dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylista*mylistb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb686ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1611de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((7,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d891204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "679a77bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "myidentity=np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c6eda9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myidentity*myidentity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "59ab9b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "myones =np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7cef8a45",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3,3) (3,4) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24416/3608483536.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyidentity\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmyones\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (3,4) "
     ]
    }
   ],
   "source": [
    "myidentity*myones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a6ca4dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myidentity@myones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "716a8626",
   "metadata": {},
   "outputs": [],
   "source": [
    "array1=np.array([[1,2,3],\n",
    "                [4,5,6]])\n",
    "array2=np.array([[2,2,2,2],\n",
    "                 [2,2,2,2],\n",
    "                 [2,2,2,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "67c9a37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 12, 12, 12],\n",
       "       [30, 30, 30, 30]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1@array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "599c6e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4a3469aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(-10,10,0.1)\n",
    "y=np.sin(x)+np.sin(4*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7a350183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e3cf028b0>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2f8687ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(xold):\n",
    "    return 3-7/(xold+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dd90b13e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "循环次数为 16 终值为 1.4142135250261267\n"
     ]
    }
   ],
   "source": [
    "xold=1\n",
    "xnew=1\n",
    "i=0\n",
    "data=np.ones((50,4))\n",
    "while True:\n",
    "    xnew=func(xold)\n",
    "    d=abs(xnew-xold)\n",
    "    data[i,:]=[i,xnew,xold,d]\n",
    "    xold=xnew\n",
    "    i=i+1\n",
    "    #print('第',i,'次 误差为',d,' 目前xnew为',xnew)\n",
    "    if d<0.0000001:\n",
    "        break\n",
    "print('循环次数为',i,'终值为',xold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0920691a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 1.25000000e+00, 1.00000000e+00, 2.50000000e-01],\n",
       "       [1.00000000e+00, 1.35294118e+00, 1.25000000e+00, 1.02941176e-01],\n",
       "       [2.00000000e+00, 1.39189189e+00, 1.35294118e+00, 3.89507154e-02],\n",
       "       [3.00000000e+00, 1.40615385e+00, 1.39189189e+00, 1.42619543e-02],\n",
       "       [4.00000000e+00, 1.41131285e+00, 1.40615385e+00, 5.15900301e-03],\n",
       "       [5.00000000e+00, 1.41317081e+00, 1.41131285e+00, 1.85795977e-03],\n",
       "       [6.00000000e+00, 1.41383887e+00, 1.41317081e+00, 6.68060431e-04],\n",
       "       [7.00000000e+00, 1.41407894e+00, 1.41383887e+00, 2.40074801e-04],\n",
       "       [8.00000000e+00, 1.41416520e+00, 1.41407894e+00, 8.62557482e-05],\n",
       "       [9.00000000e+00, 1.41419619e+00, 1.41416520e+00, 3.09882755e-05],\n",
       "       [1.00000000e+01, 1.41420732e+00, 1.41419619e+00, 1.11325649e-05],\n",
       "       [1.10000000e+01, 1.41421132e+00, 1.41420732e+00, 3.99934543e-06],\n",
       "       [1.20000000e+01, 1.41421276e+00, 1.41421132e+00, 1.43674968e-06],\n",
       "       [1.30000000e+01, 1.41421327e+00, 1.41421276e+00, 5.16146240e-07],\n",
       "       [1.40000000e+01, 1.41421346e+00, 1.41421327e+00, 1.85423269e-07],\n",
       "       [1.50000000e+01, 1.41421353e+00, 1.41421346e+00, 6.66124842e-08]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:i,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "865d5cf9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e3d044220>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdvUlEQVR4nO3de5ScdZ3n8fenb+lcyLU7F3OFJMtlEUJsQISjKF5AXQLDOMAoRsXFPSOuznF3ZXb2DO5xZg9xdXTP6MiihkRHYRDJwozXGN1hh4jY3C8BuoEmJISuJoHQnc6lu+u7f9TTsdLpTnfSlX6q6/m8zulTT/1+TxXf6tDPp+r3/Or3KCIwM7PsqUq7ADMzS4cDwMwsoxwAZmYZ5QAwM8soB4CZWUbVpF3A0WhoaIglS5akXYaZ2bjy4IMPvhoRjQPbx1UALFmyhObm5rTLMDMbVyS9OFi7h4DMzDLKAWBmllEOADOzjHIAmJlllAPAzCyjHABmZhnlADAzy6hx9T0As6Hk88G+3j56eoPefJ6+CPryf/jpzQf55La47dB98uQj6O2Lwm3SHgFB4TYfEBEEyW1AAPmi7YPtEYX9D9m3sPx68Srs/Zv9bYPtc3DfiAH7DrbPIG2D7jnUvmUuo0vYX75yASc2TC7pczoAbMzt6+mj/Y19tL+xnz37e9nb08feA32H3ibb+3r6huzfV7zdk0/7ZdkYktKuYOytXDzDAWDlbV9PHzt272PH7r28snvfYduv7N7Hzj0Hhn2e2moxsbaaiXXVTKytpr5oe9rEWurrqpk0oL++tpq6mipqqkR10U/NgO0qiZpqUV1VRbWOvK8kqsQfbhESyY8QUKWkjaQt2f5Du1BVUX/yGosPYv2tAw9sR9rnD89z+NFwsOPjUAfNwR5v2eAAsBHrPtB78CBeuN3LywPuv9bdc9jjpk+qZe7UeuZNq+fMhdOZN7WeudMKPyfU1xYO9LXV1NdVHTyY11b79JTZ8eYAsCFFBFt2dPKrLe1sfKqdx7fvPmyfmZPrmDu1njdNq+cti6czb9rEgwf7edML2xPrqlOo3syG4wCwQxzozfPAC7vY+NQr/GpLju2v70WCsxZO53PvXs7iWZOYO3Uib5pez5yp9dTX+uBuNl45AIzde3v4v8/k2PhUO//yTAed+3upr63igmWNfPai5bzzlNk0njAh7TLNrMQcABn10q7ug0M7D7ywi9580DCljve/eR7vOW0O5y9r8NCNWYVzAGREPh88vn03G59q51db2nn6lU4Als+ewr9/+0m857Q5rFgwnaoqzwgxywoHQAXb19PHb5/byS+famfTlnZynfupEpy9ZCb/7QOn8u5T57CkxPOKzWz8cABUoJ6+PGt+9jQ/fGAr3Qf6mFxXzTtObuTdp87hnSfPZsbkurRLNLMy4ACoMLv39nD9Dx/i/7W8yh+dNZ9LV7yJ85bOYkKNx/PN7FAOgAqydWc3n1j/e9pe3cOaK97MlWcvSrskMytjDoAK0dy2i+u+/yB9+eD7157LeUtnpV2SmZU5B0AF2PDwNr5w5+PMnzGR765u4qTGKWmXZGbjwLALrkhaKykn6Ylh9jtbUp+kPy5qu1jSM5JaJd1Q1D5T0kZJLcntjNG9jGzK54Ov/vIZ/vwfH+WsRdPZ8Gdv88HfzEZsJCturQMuPtIOkqqBNcAvBrR9E7gEOA24WtJpSfcNwKaIWA5sSu7bUdjX08dnbn+Yv/t1K3/StIDvX3su0yd5do+ZjdywARAR9wK7htntM8CPgVxR2zlAa0Q8HxEHgNuBVUnfKmB9sr0euOwoas68XOc+rrzlfn76+A5uuOQU1lxxBnU1Xj3TzI7OqM8BSJoPXA68Czi7qGs+8FLR/W3Aucn2nIjYARAROyTNPsLzXwdcB7BokWe1bNnxBp9c38yuPQf41offwsWnz027JDMbp0rxtvHrwBciom9A+2BrChz1tdwi4paIaIqIpsbGxmOpr2L8+ul2/vhbm+nN5/nRfzjPB38zG5VSzAJqAm5PrirUALxfUi+Fd/wLi/ZbALycbLdLmpe8+5/HoUNHNkBEcOt9bfz1T57i1HlT+e7qs5k7rT7tssxsnBt1AETEif3bktYB/xwR/0dSDbBc0onAduAq4E+TXe8BVgM3Jbd3j7aOStXTl+e//9OT/MP9W3nvaXP4+lUrmFTn2btmNnrDHkkk3QZcCDRI2gbcCNQCRMTNQz0uInolXU9hZlA1sDYinky6bwLukHQtsBX40GheRKUqXtbhU+84iS+87xSv1mlmJaOIox6WT01TU1M0NzenXcaYKF7W4W8uP93LOpjZMZP0YEQ0DWz3WEIZ8rIOZjYWHABlxss6mNlYcQCUiXw++NqvnuXvft3KuSfO5H9f8xZ/s9fMjisHQBmICD7/o0fZ8PB2/qRpAX992Zv9zV4zO+4cAGXg922vseHh7fzZhUv5z+87meQ7FWZmx5XfZpaBdZtfYNrEWj7zruU++JvZmHEApGz763v5xZPtXHXOQibW+bKNZjZ2HAAp+95v2wD46HlLUq3DzLLHAZCi7gO93P7AS7zv385h/vSJaZdjZhnjAEjRhoe3s3tvDx8//8ThdzYzKzEHQEoignX3tXH6/Kk0LfYVMc1s7DkAUnJf605acl187G0neuaPmaXCAZCSW+97gYYpdfy7M+elXYqZZZQDIAVtr+7h18/k+NNzFzOhxlM/zSwdDoAUrNvcRk2V+MhbvcSzmaXHATDGOvf1cOeD2/jgGW9i9gm+rKOZpccBMMbufHAbXft7+djblqRdipllnANgDOXzwfrNbaxcNJ0zF05PuxwzyzgHwBj6zTM52nZ2+4tfZlYWHABj6Nb72pg7tZ6LT5+bdilmZg6AsdLS3sm/tr7KNectprbav3YzS9+wRyJJayXlJD0xRP8qSY9JekRSs6QLkvaTk7b+nzckfS7p+6Kk7UV97y/pqypDt25uY0JNFVef46mfZlYeRnJFsHXAN4DvDdG/CbgnIkLSGcAdwCkR8QywAkBSNbAd2FD0uK9FxFeOse5x5fXuA9z10DYuWzGfmZN9nV8zKw/DfgKIiHuBXUfo74qISO5OBmKQ3S4CnouIF4+pynHu9t+/xL6ePB+/YEnapZiZHVSSwWhJl0t6GvgJ8IlBdrkKuG1A2/XJ0NFaSUMuhynpumRoqbmjo6MU5Y6p3r4839vcxnknzeKUuVPTLsfM7KCSBEBEbIiIU4DLgC8V90mqAy4FflTU/C1gKYUhoh3AV4/w3LdERFNENDU2Npai3DG18al2Xt69j4+dvyTtUszMDlHS6SjJcNFSSQ1FzZcAD0VEe9F+7RHRFxF54NvAOaWso5zcel8bC2dO5N2nzkm7FDOzQ4w6ACQtU7KgvaSVQB2ws2iXqxkw/COpeA3ky4FBZxiNd09s380DbbtYfd4Sqqu85r+ZlZdhZwFJug24EGiQtA24EagFiIibgSuAj0rqAfYCV/afFJY0CXgP8KkBT/tlSSsonDBuG6S/Itx6XxuT6qr5UNPCtEsxMzvMsAEQEVcP078GWDNEXzcwa5D2a0Za4HjV0bmff3r0Za48eyHTJtamXY6Z2WH8ldTj5LYHtnKgL++Tv2ZWthwAx8GB3jzfv/9F3vFvGlnaOCXtcszMBuUAOA5++vgOOjr383G/+zezMuYAKLGI4Nb7XuCkxsm8ffn4+96CmWWHA6DEHtr6Oo9u283H3raEKk/9NLMy5gAosXWb2zihvoYrVi5IuxQzsyNyAJTQK7v38bPHd3Bl00ImTxjJQqtmZulxAJTQ9+9vIx/Bal/w3czGAQdAiezr6eOHv9vKu0+dw8KZk9Iux8xsWA6AErn7ke281t3jL36Z2bjhACiBwtTPNk6ZewLnnXTYyhdmZmXJAVAC9z+/i6df6eTj5y8hWRjVzKzsOQBK4Nb7XmDGpFpWrZifdilmZiPmABill3Z1s3FLO1efs4j62uq0yzEzGzEHwCit39xGlcQ15y1OuxQzs6PiABiFPft7+cfml7jk9LnMmzYx7XLMzI6KA2AU7npoG537evn4+SemXYqZ2VFzAByjfD64dXMbZy6YxspF09Mux8zsqDkAjtG9LR0837GHj3nqp5mNUw6AY3TrfW00njCBD7z5TWmXYmZ2TBwAx+CFV/fwL8928JFzF1NX41+hmY1Pwx69JK2VlJP0xBD9qyQ9JukRSc2SLijqa5P0eH9fUftMSRsltSS3M0rzcsbGAy/sBODSFX73b2bj10jevq4DLj5C/ybgzIhYAXwC+M6A/ndGxIqIaCpquwHYFBHLk8ffMOKKy0BrrosJNVUs8qqfZjaODRsAEXEvsOsI/V0REcndyUAMtW+RVcD6ZHs9cNkIHlM2WnJdLG2cQrUv+Whm41hJBrAlXS7paeAnFD4F9Avgl5IelHRdUfuciNgBkNzOLkUdY6WlvYvlc6akXYaZ2aiUJAAiYkNEnELhnfyXirrOj4iVwCXApyW9/WifW9J1ybmF5o6OjlKUOyp79vey/fW9LJ/tADCz8a2kU1iS4aKlkhqS+y8ntzlgA3BOsmu7pHkAyW3uCM95S0Q0RURTY2NjKcs9Js91dAGwbPYJKVdiZjY6ow4AScuUfBNK0kqgDtgpabKkE5L2ycB7gf6ZRPcAq5Pt1cDdo61jrLS0FwLAQ0BmNt7VDLeDpNuAC4EGSduAG4FagIi4GbgC+KikHmAvcGVEhKQ5wIYkG2qAH0bEz5OnvQm4Q9K1wFbgQyV9VcdRS66L2mqx2DOAzGycGzYAIuLqYfrXAGsGaX8eOHOIx+wELhphjWWlNdfJSQ1TqKn2F8DMbHzzUewoteS6WObhHzOrAA6Ao7Cvp4+tu7o9A8jMKoID4Cg819FFBCxzAJhZBXAAHIXWXDIDyFNAzawCOACOQmuui+oqsaTBM4DMbPxzAByFlvYuFs+axISa6rRLMTMbNQfAUWjJdfoEsJlVDAfACB3ozdO2s9vj/2ZWMRwAI9S2cw99+fASEGZWMRwAI9S/BpCngJpZpXAAjFBLrhMJljY6AMysMjgARqgl18WimZOor/UMIDOrDA6AEWpt7/IMIDOrKA6AEejty/P8q12+CIyZVRQHwAi8uKubnr7wCWAzqygOgBE4eBUwB4CZVRAHwAj0Xwd4qQPAzCqIA2AEWto7mT99IlMmDHsBNTOzccMBMAItuS6P/5tZxXEADKMvH7TmPAXUzCqPA2AY21/by/7evNcAMrOKM2wASForKSfpiSH6V0l6TNIjkpolXZC0L5T0G0lbJD0p6bNFj/mipO3JYx6R9P7SvaTSasl1Avg7AGZWcUbyCWAdcPER+jcBZ0bECuATwHeS9l7g8xFxKvBW4NOSTit63NciYkXy89OjrnyMtOS8CJyZVaZhAyAi7gV2HaG/KyIiuTsZiKR9R0Q8lGx3AluA+aOueIy1tHcxZ+oEpk2sTbsUM7OSKsk5AEmXS3oa+AmFTwED+5cAZwG/K2q+Phk6WitpRinqOB5ac52+CIyZVaSSBEBEbIiIU4DLgC8V90maAvwY+FxEvJE0fwtYCqwAdgBfHeq5JV2XnFto7ujoKEW5IxYRngJqZhWrpLOAkuGipZIaACTVUjj4/yAi7irarz0i+iIiD3wbOOcIz3lLRDRFRFNjY2Mpyx3Wy7v30X2gzwFgZhVp1AEgaZkkJdsrgTpgZ9L2XWBLRPztgMfMK7p7OTDoDKO0tbQXZgD5OwBmVomGXdtA0m3AhUCDpG3AjUAtQETcDFwBfFRSD7AXuDIiIpkOeg3wuKRHkqf7r8mMny9LWkHhhHEb8KkSvqaSaU1mAC2f43MAZlZ5hg2AiLh6mP41wJpB2v8V0BCPuWakBaapNdfFrMl1zJxcl3YpZmYl528CH4FPAJtZJXMADCEiaGnv9BIQZlaxHABD6Ojczxv7ev0dADOrWA6AIfQvAeEZQGZWqRwAQ+ifArrMQ0BmVqEcAENoyXUxbWItjVMmpF2Kmdlx4QAYQktyEZjkO25mZhXHATCEVk8BNbMK5wAYxM6u/ezac8ABYGYVzQEwiBYvAWFmGeAAGESrp4CaWQY4AAbRmuticl0186bVp12Kmdlx4wAYREuuk2VzTvAMIDOraA6AQbS0d3n4x8wqngNggN3dPeQ69zsAzKziOQAGaO1IrgLmJSDMrMI5AAZoae+fAeQpoGZW2RwAA7TkuqivrWL+9Ilpl2Jmdlw5AAbovwpYVZVnAJlZZXMADNDa3smyRo//m1nlcwAU6drfy8u793kJCDPLBAdAkeeSJSC8CJyZZcGwASBpraScpCeG6F8l6TFJj0hqlnRBUd/Fkp6R1CrphqL2mZI2SmpJbmeU5uWMji8DaWZZMpJPAOuAi4/Qvwk4MyJWAJ8AvgMgqRr4JnAJcBpwtaTTksfcAGyKiOXJ428Y+KRpaMl1UlddxaKZk9IuxczsuBs2ACLiXmDXEfq7IiKSu5OB/u1zgNaIeD4iDgC3A6uSvlXA+mR7PXDZ0Zdeeq3tXZzUOJmaao+MmVnlK8mRTtLlkp4GfkLhUwDAfOClot22JW0AcyJiB0ByO/sIz31dMrTU3NHRUYpyh9Tiq4CZWYaUJAAiYkNEnELhnfyXkubBJtLHIG3DPfctEdEUEU2NjY2jqPLI9h7o46XXuv0NYDPLjJKOdSTDRUslNVB4x7+wqHsB8HKy3S5pHkBymytlHcfiuY4uIrwGkJllx6gDQNIyJQvnS1oJ1AE7gd8DyyWdKKkOuAq4J3nYPcDqZHs1cPdo6xgtXwXMzLKmZrgdJN0GXAg0SNoG3AjUAkTEzcAVwEcl9QB7gSuTk8K9kq4HfgFUA2sj4snkaW8C7pB0LbAV+FBJX9UxaMl1UlMlFs+anHYpZmZjYtgAiIirh+lfA6wZou+nwE8Had8JXDTCGsdES3sXSxomU1fjGUBmlg0+2iVac11eA8jMMsUBAOzv7aNt5x6fADazTHEAAG2vdpMPrwFkZtniAKBwAhh8FTAzyxYHAIUTwFWCkxo9A8jMssMBQOEE8KKZk6ivrU67FDOzMeMAoDAEtMzDP2aWMZkPgJ6+PC+86hlAZpY9mQ+AF3d209MXXgLCzDIn8wHQ6hlAZpZRmQ+AlvbCInBLZ3sGkJlliwMg18WCGROZVDfsskhmZhXFAeCrgJlZRmU6APrywXMdXT4BbGaZlOkA2PZaNwd68z4BbGaZlOkA6D8BvMzfATCzDMp2ACSXgfQ5ADPLoowHQCdzp9Yztb427VLMzMZcpgOgNdflJSDMLLMyGwD5fBQuA+nhHzPLqMwGwMu799J9oM8zgMwss4YNAElrJeUkPTFE/4clPZb8bJZ0ZtJ+sqRHin7ekPS5pO+LkrYX9b2/pK9qBPpPAHsIyMyyaiTrH6wDvgF8b4j+F4B3RMRrki4BbgHOjYhngBUAkqqB7cCGosd9LSK+cox1j1pr/xTQRgeAmWXTsAEQEfdKWnKE/s1Fd+8HFgyy20XAcxHx4lFXeJy05DppmFLHjMl1aZdiZpaKUp8DuBb42SDtVwG3DWi7Phk2WitpxlBPKOk6Sc2Smjs6OkpWqNcAMrOsK1kASHonhQD4woD2OuBS4EdFzd8CllIYItoBfHWo542IWyKiKSKaGhsbS1JrRNDa3uUTwGaWaSUJAElnAN8BVkXEzgHdlwAPRUR7f0NEtEdEX0TkgW8D55SijpHKde6nc3+vTwCbWaaNOgAkLQLuAq6JiGcH2eVqBgz/SJpXdPdyYNAZRsfLwTWAPARkZhk27ElgSbcBFwINkrYBNwK1ABFxM/BXwCzg7yUB9EZEU/LYScB7gE8NeNovS1oBBNA2SP9x1eLLQJqZjWgW0NXD9H8S+OQQfd0UwmFg+zUjLfB4aMl1MX1SLQ1TPAPIzLIrk98ELpwAnkLyicXMLJMyFwARwbO5TpZ5+MfMMi5zAbBzzwFe7+7xZSDNLPMyFwD9M4A8BdTMsi5zAdDqGUBmZkAGA6Al18WUCTXMmToh7VLMzFKVvQBoL6wB5BlAZpZ12QuAXJdPAJuZkbEAeL37AK927fcJYDMzMhYArf1XAfMJYDOzbAVA/2UgvQicmVnWAqC9i4m11cyfPjHtUszMUpetAMh1smz2FKqqPAPIzCxTAdDqGUBmZgdlJgA69/WwY/c+lnkGkJkZkKEA8AwgM7NDZSYAWg4GgD8BmJlBhgKgNddFXU0VC2dOSrsUM7OykJkAaGnv5KSGyVR7BpCZGZChAGjt6GL5HI//m5n1y0QAdB/oZdtrez3+b2ZWZNgAkLRWUk7SE0P0f1jSY8nPZklnFvW1SXpc0iOSmovaZ0raKKkluZ1RmpczuOc79hDhE8BmZsVG8glgHXDxEfpfAN4REWcAXwJuGdD/zohYERFNRW03AJsiYjmwKbl/3LT0XwXM3wEwMzto2ACIiHuBXUfo3xwRryV37wcWjOC/uwpYn2yvBy4bwWOOWUt7FzVVYvGsycfzP2NmNq6U+hzAtcDPiu4H8EtJD0q6rqh9TkTsAEhuZw/1hJKuk9Qsqbmjo+OYilo8axJ/tHI+tdWZOOVhZjYiNaV6IknvpBAAFxQ1nx8RL0uaDWyU9HTyiWLEIuIWkmGlpqamOJbarjx7EVeevehYHmpmVrFK8pZY0hnAd4BVEbGzvz0iXk5uc8AG4Jykq13SvOSx84BcKeowM7ORG3UASFoE3AVcExHPFrVPlnRC/zbwXqB/JtE9wOpkezVw92jrMDOzozPsEJCk24ALgQZJ24AbgVqAiLgZ+CtgFvD3kgB6kxk/c4ANSVsN8MOI+HnytDcBd0i6FtgKfKiEr8nMzEZAEcc0rJ6KpqamaG5uHn5HMzM7SNKDA6biAxn5JrCZmR3OAWBmllEOADOzjHIAmJll1Lg6CSypA3jxGB/eALxawnKOh3Kvsdzrg/KvsdzrA9dYCuVW3+KIaBzYOK4CYDQkNQ92FryclHuN5V4flH+N5V4fuMZSKPf6+nkIyMwsoxwAZmYZlaUAGHidgnJU7jWWe31Q/jWWe33gGkuh3OsDMnQOwMzMDpWlTwBmZlbEAWBmllGZCABJF0t6RlKrpON6/eGjJWmhpN9I2iLpSUmfTbumoUiqlvSwpH9Ou5aBJE2XdKekp5Pf5Xlp1zSQpD9P/o2fkHSbpPoyqGmtpJykJ4raZkraKKkluZ1RZvX9z+Tf+TFJGyRNT6u+pJ7Daizq+0+SQlJDGrUNp+IDQFI18E3gEuA04GpJp6Vb1SF6gc9HxKnAW4FPl1l9xT4LbEm7iCH8L+DnEXEKcCZlVqek+cB/BJoi4nSgGrgq3aoAWAdcPKDtBmBTRCwHNiX307KOw+vbCJweEWcAzwJ/MdZFDbCOw2tE0kLgPRSWvC9LFR8AFK5C1hoRz0fEAeB2ChelLwsRsSMiHkq2OykcuOanW9XhJC0APkDhym9lRdJU4O3AdwEi4kBEvJ5qUYOrASZKqgEmAS+nXA/JJVp3DWheBaxPttcDl41lTcUGqy8ifhkRvcnd+4EFY17YofUM9jsE+BrwXyhcG70sZSEA5gMvFd3fRhkeYAEkLQHOAn6XcimD+TqF/5nzKdcxmJOADuDWZIjqO8lV6MpGRGwHvkLh3eAOYHdE/DLdqoY0JyJ2QOENCjA75XqO5BPAz9IuYiBJlwLbI+LRtGs5kiwEgAZpK7tEljQF+DHwuYh4I+16ikn6IJCLiAfTrmUINcBK4FsRcRawh3SHLQ6TjKOvAk4E3gRMlvSRdKsa3yT9JYUh1B+kXUsxSZOAv6RwtcSyloUA2AYsLLq/gDL46F1MUi2Fg/8PIuKutOsZxPnApZLaKAyhvUvSP6Rb0iG2Adsiov+T050UAqGcvBt4ISI6IqKHwnW035ZyTUNplzQPILnNpVzPYSStBj4IfDjK78tMSykE/aPJ38wC4CFJc1OtahBZCIDfA8slnSipjsKJt3tSrukgFS6a/F1gS0T8bdr1DCYi/iIiFkTEEgq/v19HRNm8e42IV4CXJJ2cNF0EPJViSYPZCrxV0qTk3/wiyuxEdZF7gNXJ9mrg7hRrOYyki4EvAJdGRHfa9QwUEY9HxOyIWJL8zWwDVib/n5aVig+A5GTR9cAvKPzB3RERT6Zb1SHOB66h8K76keTn/WkXNQ59BviBpMeAFcD/SLecQyWfTu4EHgIep/C3l/pyAZJuA34LnCxpm6RrgZuA90hqoTCL5aYyq+8bwAnAxuTv5ea06jtCjeOCl4IwM8uoiv8EYGZmg3MAmJlllAPAzCyjHABmZhnlADAzyygHgJlZRjkAzMwy6v8DCc4EeYpNvawAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:i,0],data[:i,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ff9ac35b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=[1.414]*len(data[:i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ff170ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25e3d2dbeb0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pBYuZ3U4iSGYGTZtIXNdYBfy3u9eHW17/0NjcyrZdDSw8QUsQi0jfl+oRy40kjkz+HfiWu28NvaJ+6I2aetxhmoYai0gWSHVU2M+BSuBK4DUzW2Nm3zGzs82sIPTq+omKWDAiTEONRSQLpBQs7n59MA/XJOB6oJzEsN5VwPtmtsrMvhZ+mdmtIlZPbo5RUqQRYSLS9x3SnfTuXuXuy9x9sbtPAi4DtgJns/8d8tIN5dX1TB41iAF5fWownYhIhw51VNhUPhoVdjZQDBiJSSX/K6zi+ovyWJ2mchGRrJHqqLB7SYTJRBJBUg88CzwTfG30nt4Y0880tcSprG1g/sxxUZciIhKKVI9YrgCeB35GIkjWuntr6FX1I5W1H9Iad03lIiJZI9VgOczd96alkn6qbY4wDTUWkWyR6qiwfaFiZgPMbLyGGfdMeawOM5harGARkeyQ8qgwM5ttZs8AdSQmejwtaB8dDDc+N+Qas1p5rJ5JIwdRmK8RYSKSHVJd6GsWiYv1U4FfJvcFa6MM5KNp66UbKqrrNSJMRLJKqkcs/wq8A3wMWEJiZFiyVXy06JYcREtrnDd31mtxLxHJKqkGy+nAz4LJJjsaVrwNOLzHVfUTb+1qoLnVdeFeRLJKqsFSCOzuon9YD2rpd/atGqlgEZEskmqwvAF8vIv+TwCbD72c/qVtnfupChYRySKpBssK4Mp2I78cwMy+AswDfhVSbVmvvLqO8SMGMmTAIc2sIyKSkVL9iXYbcB7wBPAqiVD5YbCi5FjgKeCuUCvMYuWxel1fEZGsk+oNkk0kguWfgD1AI3AksBP4KnCBu8fDLjIbtcadipiGGotI9kn5HIy7twA/DL7kEG1/bw97W+KaI0xEss4hrccSBjO718xiZvZyJ/0LzOwvZrbRzMrMrO0O/4lm9icz22Jmr5jZl5Le8y0z2x68Z6OZnd9bnydV5bE6AN3DIiJZJ+UjFjMz4FxgOjCKA2+SdHfvzmJfy4A7aXcHf5JVwKPu7mZ2HPAgcDTQAnzF3V80s6HAejN7yt3bRqP90N1vS+lDRaA8psknRSQ7pboey3TgP0j8gG8fKG2cbqwi6e6rzayki/76pJeDg/3i7juAHcHzOjPbAoynjw1zLq+uZ8ywAQwfmB91KSIioUr1iOUOEvOE3URiPZba0CtKYmYLgf8LjAY+1UF/CXAC8Oek5hvM7LNAGYkjm/fSWeOhqojVMV2nwUQkC6V6jeU04Efufpu7v+jub3X0FVZx7r7S3Y8GLqLdUZCZDQF+B9zo7h8EzT8lEXyzSBzV/KCzfZvZ9cG1m7KampqwSu4Wd9dQYxHJWqkGSxOwNR2FdMXdVwNTzawIwMzySYTKr9394aTtqt29NRjy/DO6mBDT3e9x91J3Ly0uLk7zJ9jfO7sbaWhqVbCISFZKNVieAOamo5D2zGxaMFAAM5sNFAC1QdsvgC3ufnu79yQvHL8Q6HDEWdTKqxMjwnQPi4hko1SvsXwZWB1M33JHcMPkITGz+4GzgCIzqwJuBvIB3P1u4BLgs2bWTOJmzMuDEWKnAVcCm8xsY7C7r7n7Y8D3gjVjHKgEPneo9aVTRTAibPoYXWMRkeyTarA8R2KE1veAW83sHaC13Tbu7lMPtiN3X3SQ/qXA0g7a/4dORqS5+5UH+3MzQUWsnlGDCxg5WKs6i0j2STVYttHxOiySAl24F5FsllKwuPtZaaqj33B3yqvruHCW1kMTkeyU6pr332q7oN5J/2Fm9h89riqL1dTt5YPGFt3DIiJZK9VRYd8E/mRmE9p3mNmZwCZgfhiFZau2qVw0IkxEslWqwfJ54ERgo5ktADCzHDP7DvA00AycGW6J2aVtqPE0zWosIlkq1fVY7iFx0+EO4GEzuxtYDXwdeBiY5e4vhF5lFimP1TN8YD7FQwZEXYqISFocynosr5jZiSSOUP4uaP6au98aamVZqjxY3KuLS1UiIn1ayuuxmFkBiftYTgXeJDGN/Q3BNRY5iAoNNRaRLJfqqLAjgReAG0hM+DgTOJ3EHGJPm9m/mllki4dlutr6vez6sEnBIiJZLdUQWA+UAJe4+z+4+153XwscDzwE/AvwX6FWmEXKNZWLiPQDqQbLSyQu0K9MbnT3umCKlr8DZodVXLap0FBjEekHUr14f6a7t58bbB93/4WZ/U8Pa8paFbF6BhfkMm54YdSliIikTarDjTsNlaRtXjv0crJbeayOaWOGakSYiGQ1XWjvReXV9ToNJiJZT8HSS3Y3NBOr26tgEZGsp2DpJRU1waqRmspFRLKcgqWXlFe3jQjTUGMRyW4Kll5SHqunMD+H8SMGRl2KiEhaKVh6SduqkTk5GhEmItlNwdJLKqrrmFas6ysikv0ULL2gfm8L7+xu1FQuItIvKFh6wRvBVC6afFJE+oPIgsXM7jWzmJm93En/AjP7i5ltNLMyMzstqW+emb1mZhVmtiSpfaSZPWVm5cHjYb3xWQ5GyxGLSH8S5RHLMmBeF/2rgOPdfRZwDfBzADPLBX4CzAdmAIvMbEbwniXAKnefHrx/SfudRqE8VkdBbg6TRg6KuhQRkbSLLFjcfTWwq4v+enf34OVgoO35SUCFu7/p7k3AA8CCoG8BsDx4vhy4KOy6D0VFdT1HFA8mL1dnHkUk+2X0TzozW2hmrwJ/IHHUAjAeeDtps6qgDWCMu+8ACB5Hd7Hv64NTbGU1NTXhF5+kXKtGikg/ktHB4u4r3f1oEkce3wmaO7oRxDtoO9i+73H3UncvLS4u7kGVXdvT1Mrb7zXojnsR6TcyOljaBKfNpppZEYkjlIlJ3ROAd4Ln1WY2DiB4jPVqoR14o6Yed80RJiL9R8YGi5lNs2DhEjObDRQAtcA6YLqZTTGzAuAK4NHgbY8Ci4Pni4FHerfqA2nVSBHpb1JdQTI0ZnY/cBZQZGZVwM1APoC73w1cAnzWzJqBPcDlwcX8FjO7AXgCyAXudfdXgt3eCjxoZtcC24BLe/Ejdag8VkdejjF51OCoSxER6RWRBYu7LzpI/1JgaSd9jwGPddBeC5wTSoEhKa+up6RoMAV5GXtwKCISKv20S7OKWL3mCBORfkXBkkZ7W1qprP1QF+5FpF9RsKRR5c4G4q45wkSkf1GwpFF5LFiOWPewiEg/omBJo/LqenIMjijWiDAR6T8ULGlUEatn0shBFObnRl2KiEivUbCkUXmsjmk6DSYi/YyCJU2aW+Ns3akRYSLS/yhY0uSt2gaaW11TuYhIv6NgSZMKjQgTkX5KwZIm5dWJySenjtaIMBHpXxQsaVIeq2fCYQMZVBDZdGwiIpFQsKSJVo0Ukf5KwZIGrXHnjZp6XbgXkX5JwZIGVe810NQS14V7EemXFCxp0HbhfpruYRGRfkjBkgblwXLEusYiIv2RgiUNymN1jB1WyLDC/KhLERHpdQqWNKiI1WsqFxHptxQsIYvHPbEcsU6DiUg/pWAJ2Tu799DQ1KoRYSLSb0UWLGZ2r5nFzOzlTvo/Y2Z/Cb7WmNnxQftRZrYx6esDM7sx6PuWmW1P6ju/Fz8S8NGFe50KE5H+Ksr5RpYBdwK/7KR/K3Cmu79nZvOBe4CT3f01YBaAmeUC24GVSe/7obvflq6iD6aibahxsYJFRPqnyILF3VebWUkX/WuSXr4ATOhgs3OAN9z9rZDLO2TlsTqKhhRw2OCCqEsREYlEX7nGci3weAftVwD3t2u7ITh9dq+ZHdbZDs3sejMrM7Oympqa0ArVHGEi0t9l/NS7ZnY2iWA5rV17AXAh8M9JzT8FvgN48PgD4JqO9uvu95A4vUZpaamHUau7U1Fdz0UnjA9jdyISsubmZqqqqmhsbIy6lD6lsLCQCRMmkJ/fvXvzMjpYzOw44OfAfHevbdc9H3jR3avbGpKfm9nPgN/3SqGBWN1e6va26MK9SIaqqqpi6NChlJSUYGZRl9MnuDu1tbVUVVUxZcqUbr0nY0+Fmdkk4GHgSnd/vYNNFtHuNJiZjUt6uRDocMRZuuybI0ynwkQyUmNjI6NGjVKopMDMGDVqVEpHeZEdsZjZ/cBZQJGZVQE3A/kA7n438E1gFHBX8J+gxd1Lg/cOAs4DPtdut98zs1kkToVVdtCfVuVajlgk4ylUUpfq31mUo8IWHaT/OuC6TvoaSIRO+/Yrw6nu0JTH6hkxKJ+iIRoRJiL9V8aeCuuLKqoTi3vpNyIR6c8ULCFxd16P1TFNp8FEpAunnnpq1CWkXUaPCutLaj9s4v2GZi1HLNJHfPs/X2HzOx+Eus8Zhw/j5r/+WJfbrFmzpsv+bKAjlpC0jQjTUGMR6cqQIZ3/jFi5ciXnnnsu7s6OHTs48sgjeffdd1m2bBkXX3wx8+bNY/r06Xz1q1/d954nn3ySOXPmMHv2bC699FLq6+tZu3YtF198MQCPPPIIAwcOpKmpicbGRo444oi0f0YdsYSkQiPCRPqUgx1ZRGHhwoX87ne/4yc/+Ql//OMf+fa3v83YsWMB2LhxIxs2bGDAgAEcddRRfOELX2DgwIHccsstPP300wwePJilS5dy++2387WvfY0NGzYA8OyzzzJz5kzWrVtHS0sLJ598cto/h4IlJOWxeoYMyGPMsAFRlyIifdgdd9zBzJkzOeWUU1i06KPBs+eccw7Dhw8HYMaMGbz11lu8//77bN68mblz5wLQ1NTEnDlzyMvLY9q0aWzZsoW1a9fy5S9/mdWrV9Pa2srpp5+e9s+gYAlJeXVijjCNCBORnti+fTs5OTlUV1cTj8fJyUlcsRgw4KNfWnNzc2lpacHdOe+887j//vZTJsLpp5/O448/Tn5+Pueeey5XXXUVra2t3HZb+id/1zWWkJTH6nXhXkR6pKWlhauvvpoVK1ZwzDHHcPvtt3e5/SmnnMJzzz1HRUUFAA0NDbz+emKikjPOOIMf/ehHzJkzh+LiYmpra3n11Vf52MfSfwpQRywheL+hiZ31e3XhXkR65Lvf/S6nn346p59+OrNmzeLEE0/kU5/6VKfbFxcXs2zZMhYtWsTevXsBuOWWWzjyyCM5+eSTqa6u5owzzgDguOOOY/To0b1yVsXcQ5nYt08rLS31srKyQ35/WeUuPn3389x31YmcffToECsTkTBt2bKFY445Juoy+qSO/u7MbH3bVFvJdCosBG3LEWvySRERnQoLRXl1PQPzcxk/YmDUpYhIH7Bp0yauvHL/qQ0HDBjAn//854gqCpeCJQTlsTqmjR5CTo5GhInIwR177LFs3Lgx6jLSRqfCQlChEWEiIvsoWHqorrGZHbsbmaYRYSIigIKlxyqCC/eaykVEJEHB0kPl+4JFRywikh533nkn06ZNw8zYuXNnl9t+8MEHjB8/nhtuuKHb71+3bh25ubk89NBDodSrYOmhilg9BXk5TBw5KOpSRCRLzZ07l6effprJkycfdNtvfOMbnHnmmd1+f2trKzfddBOf/OQnQ6tXo8J6qLy6jiOKBpOrEWEifcvjS+DdTeHuc+yxMP/WTru/8Y1vUFRUxJe+9CUAvv71rzNmzBi++MUvdrnbE044oVt//Pr166murmbevHkk3/Td1fvvuOMOLrnkEtatW9etP6M7dMTSQxU19Uwfo+srInJw1157LcuXLwcgHo/zwAMPsGDBAmbNmtXh1+bNm7u973g8zle+8hW+//3vd/s927dvZ+XKlXz+859P+bN0RUcsPdDQ1ELVe3u49OMToy5FRFLVxZFFupSUlDBq1Cg2bNhAdXU1J5xwApMnTw7lnpa77rqL888/n4kTu//z6MYbb2Tp0qXk5ub2+M9PFlmwmNm9wAVAzN1ndtD/GeCm4GU98L/c/aWgrxKoA1qBlra5asxsJPAboASoBC5z9/fS9RnerPkQd124F5Huu+6661i2bBnvvvsu11xzDXV1dZ2ukbJixQpmzJjRrf0+//zzPPvss9x1113U19fT1NTEkCFDuPXWzgO0rKyMK664AoCdO3fy2GOPkZeXx0UXXZTy50oW5RHLMuBO4Jed9G8FznT398xsPnAPkLz02dnu3n54wxJglbvfamZLgtc3kSblbatG6h4WEemmhQsX8s1vfpPm5mZWrFhBbm5uKEcsv/71r/c9X7ZsGWVlZV2GCsDWrVv3Pb/qqqu44IILehwqEOE1FndfDezqon9N0tHGC8CEbux2AbA8eL4cuKgnNR5MeXU9eTnG5FGD0/nHiEgWKSgo4Oyzz+ayyy7r9imoH//4x0yYMIGqqiqOO+44rrvuOiBxxNH2/FDeny6RTptvZiXA7zs6FdZuu38Cjnb364LXW4H3AAf+zd3vCdrfd/cRSe97z90P62Sf1wPXA0yaNOnjb731Vsr1/2bdNta/9R7f+/TxKb9XRHpfJkybH4/HmT17Nr/97W+ZPn16pLWkIqumzTezs4Fr2f+U1lx3nw3MB/7BzM5Idb/ufo+7l7p7aXFx8SHVdvmJkxQqItJtmzdvZtq0aZxzzjl9KlRSldGjwszsOODnwHx3r21rd/d3gseYma0ETgJWA9VmNs7dd5jZOCAWRd0iIh2ZMWMGb775ZtRlpF3GHrGY2STgYeBKd389qX2wmQ1tew78FfBy0P0osDh4vhh4pPcqFpG+QKvmpi7Vv7MohxvfD5wFFJlZFXAzkA/g7ncD3wRGAXcFazS3DSseA6wM2vKAFe7+x2C3twIPmtm1wDbg0l77QCKS8QoLC6mtrWXUqFG9svZ7NnB3amtrKSws7PZ7tOY9PV/zXkT6hubmZqqqqmhsbIy6lD6lsLCQCRMmkJ+fv197ZxfvM/oai4hImPLz85kyZUrUZWS9jL3GIiIifZOCRUREQqVgERGRUOniPWBmNUDqt94nFAFdL+kWvUyvMdPrg8yvMdPrA9UYhkyrb7K7H3CHuYKlh8ysrKNREZkk02vM9Pog82vM9PpANYYh0+tro1NhIiISKgWLiIiESsHSc/dEXUA3ZHqNmV4fZH6NmV4fqMYwZHp9gK6xiIhIyHTEIiIioVKwiIhIqBQsPWBm88zsNTOrMLMlUdeTzMwmmtmfzGyLmb1iZl+KuqbOmFmumW0ws99HXUt7ZjbCzB4ys1eDv8s5UdfUnpn9Y/Bv/LKZ3W9m3Z+GNn013WtmMTN7OaltpJk9ZWblwWOHq7tGWN/3g3/nv5jZSjMbEVV9QT0H1JjU909m5mZWFEVtB6NgOURmlgv8hMQqljOARWY2I9qq9tMCfMXdjwFOIbHSZibVl+xLwJaoi+jE/wP+6O5HA8eTYXWa2Xjgi0BpsMR3LnBFtFUBsAyY165tCbDK3acDq4LXUVnGgfU9Bcx09+OA14F/7u2i2lnGgTViZhOB80gsDZKRFCyH7iSgwt3fdPcm4AFgQcQ17ePuO9z9xeB5HYkfiOOjrepAZjYB+BSJlUIzipkNA84AfgHg7k3u/n6kRXUsDxhoZnnAIOCdiOvB3VcDu9o1LwCWB8+XAxf1Zk3JOqrP3Z9095bg5QvAhF4vbP96Ovo7BPgh8FUgY0deKVgO3Xjg7aTXVWTgD24AMysBTgD+HHEpHfkRiW+SeMR1dOQIoAa4LzhV9/Ng1dKM4e7bgdtI/Pa6A9jt7k9GW1Wnxrj7Dkj84gOMjrierlwDPB51Ee2Z2YXAdnd/KepauqJgOXQdLT+Xcb9BmNkQ4HfAje7+QdT1JDOzC4CYu6+PupZO5AGzgZ+6+wnAh0R7+uYAwXWKBcAU4HBgsJn9bbRV9W1m9nUSp5J/HXUtycxsEPB1EqvrZjQFy6GrAiYmvZ5ABpyCSGZm+SRC5dfu/nDU9XRgLnChmVWSOJX4CTP792hL2k8VUOXubUd6D5EImkxyLrDV3WvcvRl4GDg14po6U21m4wCCx1jE9RzAzBYDFwCf8cy7yW8qiV8gXgq+ZyYAL5rZ2Eir6oCC5dCtA6ab2RQzKyBxwfTRiGvaxxILev8C2OLut0ddT0fc/Z/dfYK7l5D4+3vG3TPmt213fxd428yOCprOATZHWFJHtgGnmNmg4N/8HDJsgEGSR4HFwfPFwCMR1nIAM5sH3ARc6O4NUdfTnrtvcvfR7l4SfM9UAbOD/6cZRcFyiIKLfDcAT5D4Rn7Q3V+Jtqr9zAWuJHEUsDH4Oj/qovqgLwC/NrO/ALOA70Zbzv6Co6mHgBeBTSS+pyOf9sPM7geeB44ysyozuxa4FTjPzMpJjGq6NcPquxMYCjwVfL/cHVV9XdTYJ2hKFxERCZWOWEREJFQKFhERCZWCRUREQqVgERGRUClYREQkVAoWkQxmZmcFs9heFXUtIt2lYBERkVDpPhaRDGZmOUAB0OzurVHXI9IdChYREQmVToWJZDBdY5G+SMEiIiKhUrCIiEioFCwiIhIqBYuIiIRKwSIiIqFSsIiISKgULCIiEioFi4iIhErBIiIiodKULiIiEiodsYiISKgULCIiEioFi4iIhErBIiIioVKwiIhIqBQsIiISKgWLiIiESsEiIiKhUrCIiEio/j+YkyQ48BTl/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[:i,0],data[:i,1])\n",
    "plt.plot(data[:i,0],y1)\n",
    "plt.xlabel(\"i\",fontsize=18)\n",
    "plt.ylabel(\"xnew\",fontsize=18)\n",
    "plt.legend([\"i_xnew\",\"y=1.414\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe5ffe0b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
